/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    Standardize.java
 *    Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.attribute;

import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.core.WeightedAttributesHandler;
import weka.core.WeightedInstancesHandler;
import weka.filters.Sourcable;
import weka.filters.UnsupervisedFilter;

/**
 * <!-- globalinfo-start --> Standardizes all numeric attributes in the given
 * dataset to have zero mean and unit variance (apart from the class attribute,
 * if set).
 * <p/>
 * <!-- globalinfo-end -->
 *
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 *  -unset-class-temporarily
 *  Unsets the class index temporarily before the filter is
 *  applied to the data.
 *  (default: no)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision$
 */
public class Standardize extends PotentialClassIgnorer implements UnsupervisedFilter, Sourcable, WeightedAttributesHandler, WeightedInstancesHandler {

    /** for serialization */
    static final long serialVersionUID = -6830769026855053281L;

    /** The means */
    private double[] m_Means;

    /** The variances */
    private double[] m_StdDevs;

    /**
     * Returns a string describing this filter
     *
     * @return a description of the filter suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String globalInfo() {

        return "Standardizes all numeric attributes in the given dataset " + "to have zero mean and unit variance (apart from the class attribute, if set).";
    }

    /**
     * Returns the Capabilities of this filter.
     *
     * @return the capabilities of this object
     * @see Capabilities
     */
    public Capabilities getCapabilities() {
        Capabilities result = super.getCapabilities();
        result.disableAll();

        // attributes
        result.enableAllAttributes();
        result.enable(Capability.MISSING_VALUES);

        // class
        result.enableAllClasses();
        result.enable(Capability.MISSING_CLASS_VALUES);
        result.enable(Capability.NO_CLASS);

        return result;
    }

    /**
     * Sets the format of the input instances.
     *
     * @param instanceInfo an Instances object containing the input instance
     *                     structure (any instances contained in the object are
     *                     ignored - only the structure is required).
     * @return true if the outputFormat may be collected immediately
     * @throws Exception if the input format can't be set successfully
     */
    public boolean setInputFormat(Instances instanceInfo) throws Exception {

        super.setInputFormat(instanceInfo);
        setOutputFormat(instanceInfo);
        m_Means = m_StdDevs = null;
        return true;
    }

    /**
     * Input an instance for filtering. Filter requires all training instances be
     * read before producing output.
     *
     * @param instance the input instance
     * @return true if the filtered instance may now be collected with output().
     * @throws IllegalStateException if no input format has been set.
     */
    public boolean input(Instance instance) throws Exception {

        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }
        if (m_NewBatch) {
            resetQueue();
            m_NewBatch = false;
        }
        if (m_Means == null) {
            bufferInput(instance);
            return false;
        } else {
            convertInstance(instance);
            return true;
        }
    }

    /**
     * Signify that this batch of input to the filter is finished. If the filter
     * requires all instances prior to filtering, output() may now be called to
     * retrieve the filtered instances.
     *
     * @return true if there are instances pending output
     * @exception Exception             if an error occurs
     * @exception IllegalStateException if no input structure has been defined
     */
    public boolean batchFinished() throws Exception {

        if (getInputFormat() == null) {
            throw new IllegalStateException("No input instance format defined");
        }
        if (m_Means == null) {
            Instances input = getInputFormat();
            m_Means = new double[input.numAttributes()];
            m_StdDevs = new double[input.numAttributes()];
            for (int i = 0; i < input.numAttributes(); i++) {
                if (input.attribute(i).isNumeric() && (input.classIndex() != i)) {
                    m_Means[i] = input.meanOrMode(i);
                    m_StdDevs[i] = Math.sqrt(input.variance(i));
                }
            }

            // Convert pending input instances
            for (int i = 0; i < input.numInstances(); i++) {
                convertInstance(input.instance(i));
            }
        }
        // Free memory
        flushInput();

        m_NewBatch = true;
        return (numPendingOutput() != 0);
    }

    /**
     * Convert a single instance over. The converted instance is added to the end of
     * the output queue.
     *
     * @param instance the instance to convert
     * @exception Exception if an error occurs
     */
    private void convertInstance(Instance instance) throws Exception {

        Instance inst = null;
        if (instance instanceof SparseInstance) {
            double[] newVals = new double[instance.numAttributes()];
            int[] newIndices = new int[instance.numAttributes()];
            double[] vals = instance.toDoubleArray();
            int ind = 0;
            for (int j = 0; j < instance.numAttributes(); j++) {
                double value;
                if (instance.attribute(j).isNumeric() && (!Utils.isMissingValue(vals[j])) && (getInputFormat().classIndex() != j)) {

                    // Just subtract the mean if the standard deviation is zero
                    if (m_StdDevs[j] > 0) {
                        value = (vals[j] - m_Means[j]) / m_StdDevs[j];
                    } else {
                        value = vals[j] - m_Means[j];
                    }
                    if (Double.isNaN(value)) {
                        throw new Exception("A NaN value was generated " + "while standardizing attribute " + instance.attribute(j).name());
                    }
                    if (value != 0.0) {
                        newVals[ind] = value;
                        newIndices[ind] = j;
                        ind++;
                    }
                } else {
                    value = vals[j];
                    if (value != 0.0) {
                        newVals[ind] = value;
                        newIndices[ind] = j;
                        ind++;
                    }
                }
            }
            double[] tempVals = new double[ind];
            int[] tempInd = new int[ind];
            System.arraycopy(newVals, 0, tempVals, 0, ind);
            System.arraycopy(newIndices, 0, tempInd, 0, ind);
            inst = new SparseInstance(instance.weight(), tempVals, tempInd, instance.numAttributes());
        } else {
            double[] vals = instance.toDoubleArray();
            for (int j = 0; j < getInputFormat().numAttributes(); j++) {
                if (instance.attribute(j).isNumeric() && (!Utils.isMissingValue(vals[j])) && (getInputFormat().classIndex() != j)) {

                    // Just subtract the mean if the standard deviation is zero
                    if (m_StdDevs[j] > 0) {
                        vals[j] = (vals[j] - m_Means[j]) / m_StdDevs[j];
                    } else {
                        vals[j] = (vals[j] - m_Means[j]);
                    }
                    if (Double.isNaN(vals[j])) {
                        throw new Exception("A NaN value was generated " + "while standardizing attribute " + instance.attribute(j).name());
                    }
                }
            }
            inst = new DenseInstance(instance.weight(), vals);
        }
        inst.setDataset(instance.dataset());
        push(inst, false); // No need to copy
    }

    /**
     * Returns a string that describes the filter as source. The filter will be
     * contained in a class with the given name (there may be auxiliary classes),
     * and will contain two methods with these signatures:
     * 
     * <pre>
     * <code>
     * // converts one row
     * public static Object[] filter(Object[] i);
     * // converts a full dataset (first dimension is row index)
     * public static Object[][] filter(Object[][] i);
     * </code>
     * </pre>
     * 
     * where the array <code>i</code> contains elements that are either Double,
     * String, with missing values represented as null. The generated code is public
     * domain and comes with no warranty.
     *
     * @param className the name that should be given to the source class.
     * @param data      the dataset used for initializing the filter
     * @return the object source described by a string
     * @throws Exception if the source can't be computed
     */
    public String toSource(String className, Instances data) throws Exception {
        StringBuffer result;
        boolean[] process;
        int i;

        result = new StringBuffer();

        // determine what attributes were processed
        process = new boolean[data.numAttributes()];
        for (i = 0; i < data.numAttributes(); i++) {
            process[i] = (data.attribute(i).isNumeric() && (i != data.classIndex()));
        }

        result.append("class " + className + " {\n");
        result.append("\n");
        result.append("  /** lists which attributes will be processed */\n");
        result.append("  protected final static boolean[] PROCESS = new boolean[]{" + Utils.arrayToString(process) + "};\n");
        result.append("\n");
        result.append("  /** the computed means */\n");
        result.append("  protected final static double[] MEANS = new double[]{" + Utils.arrayToString(m_Means) + "};\n");
        result.append("\n");
        result.append("  /** the computed standard deviations */\n");
        result.append("  protected final static double[] STDEVS = new double[]{" + Utils.arrayToString(m_StdDevs) + "};\n");
        result.append("\n");
        result.append("  /**\n");
        result.append("   * filters a single row\n");
        result.append("   * \n");
        result.append("   * @param i the row to process\n");
        result.append("   * @return the processed row\n");
        result.append("   */\n");
        result.append("  public static Object[] filter(Object[] i) {\n");
        result.append("    Object[] result;\n");
        result.append("\n");
        result.append("    result = new Object[i.length];\n");
        result.append("    for (int n = 0; n < i.length; n++) {\n");
        result.append("      if (PROCESS[n] && (i[n] != null)) {\n");
        result.append("        if (STDEVS[n] > 0)\n");
        result.append("          result[n] = (((Double) i[n]) - MEANS[n]) / STDEVS[n];\n");
        result.append("        else\n");
        result.append("          result[n] = ((Double) i[n]) - MEANS[n];\n");
        result.append("      }\n");
        result.append("      else {\n");
        result.append("        result[n] = i[n];\n");
        result.append("      }\n");
        result.append("    }\n");
        result.append("\n");
        result.append("    return result;\n");
        result.append("  }\n");
        result.append("\n");
        result.append("  /**\n");
        result.append("   * filters multiple rows\n");
        result.append("   * \n");
        result.append("   * @param i the rows to process\n");
        result.append("   * @return the processed rows\n");
        result.append("   */\n");
        result.append("  public static Object[][] filter(Object[][] i) {\n");
        result.append("    Object[][] result;\n");
        result.append("\n");
        result.append("    result = new Object[i.length][];\n");
        result.append("    for (int n = 0; n < i.length; n++) {\n");
        result.append("      result[n] = filter(i[n]);\n");
        result.append("    }\n");
        result.append("\n");
        result.append("    return result;\n");
        result.append("  }\n");
        result.append("}\n");

        return result.toString();
    }

    /**
     * Main method for testing this class.
     *
     * @param argv should contain arguments to the filter: use -h for help
     */
    public static void main(String[] argv) {
        runFilter(new Standardize(), argv);
    }
}
